#Set working directory to appropriate folder for inputs and outputs on Google Drive
#Initialize
rm(list = ls())
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
library(Seurat)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
Attaching sp
library(ggplot2)
library(RColorBrewer)
`%nin%` = Negate(`%in%`)
#Plot cell cycle
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')
#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))
filtered_meta <- rep(0, length(names(all_data$Lineage)))
#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'
#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'
# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'
#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'
print(table(filtered_meta))
#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))
filtered_meta <- rep(0, length(names(all_data$Lineage)))
#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'
#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'
# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'
#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'
print(table(filtered_meta))
filtered_meta
Filtered out Large Resistant to Cis Large Resistant to CistoCis Large Resistant to CistoCoCl2 Large Resistant to CistoDabTram
3337 1375 951 2078 1394
Large Resistant to CoCl2 Large Resistant to CoCl2toCis Large Resistant to CoCl2toCoCl2 Large Resistant to CoCl2toDabTram Large Resistant to DabTram
1784 3010 11578 663 478
Large Resistant to DabTramtoCis Large Resistant to DabTramtoCoCl2 Large Resistant to DabTramtoDabTram No Barcode Resistant to Cis
4234 2840 3176 35314 331
Resistant to CistoCis Resistant to CistoCoCl2 Resistant to CistoDabTram Resistant to CoCl2 Resistant to CoCl2toCis
67 135 113 278 157
Resistant to CoCl2toCoCl2 Resistant to CoCl2toDabTram Resistant to DabTram Resistant to DabTramtoCis Resistant to DabTramtoCoCl2
55 93 337 225 100
Resistant to DabTramtoDabTram
222
all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filtered
```r
all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filtered
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<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#Looking into the one lineage that switches ngfr -> egfr
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucGRmKCcyMDIyXzAxXzE0X2FuYWx5c2lzX3NjcmlwdHMvMjAyMl8wNV8yN19hbmFseXNpcy9MaW5lYWdlX2V4cHJlc3Npb24vdGVzdF9wbG90LnBkZicsIGhlaWdodCA9IDEwLCB3aWR0aCA9IDIwKVxuRGltUGxvdChhbGxfZGF0YSwgZ3JvdXAuYnkgPSAnaWRlbnQnLCBjb2xzID0gKVxuRGltUGxvdChhbGxfZGF0YSwgZ3JvdXAuYnkgPSBcXExpbmVhZ2VcXCkgKyB0aGVtZShsZWdlbmQucG9zaXRpb24gPSAnbm9uZScpXG5gYGBcbmBgYHJcbmRldi5vZmYoKVxuYGBgXG5gYGAifQ== -->
```r
```r
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_plot.pdf', height = 10, width = 20)
DimPlot(all_data, group.by = 'ident', cols = )
DimPlot(all_data, group.by = \Lineage\) + theme(legend.position = 'none')
dev.off()
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<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
null device 1
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<!-- rnb-text-begin -->
#from dabtram_both_times, force 2 clusters in umap, plot vs ngfr egfr, find markers of these 2
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
switch_lin_dabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtram'])
switch_lin_dabtramtodabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtramtodabtram'])
DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram), cols.highlight = c('red'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram, switch_lin_dabtramtodabtram), cols.highlight = c('blue', 'red'))
switch_lin <- names(dabtram$orig.ident[dabtram$Lineage == "Lin171516"])
DimPlot(dabtram)
DimPlot(dabtram, cells.highlight = list(switch_lin))
#need to integrate lineages into just dabtram object and then plot off of this isntead?
#vlnplot(all_data, features = ngfr, idents = just the cells resistant to dabtram, group.by = lineage but i only want those included in combined_lins_list$dabtram ?
#Assign cluster assignments per lineage, find average score per lineage - make plots in order
dabtram_both_times_markers <- FindAllMarkers(dabtram_both_times, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~19s
|++ | 2 % ~18s
|++ | 3 % ~18s
|+++ | 4 % ~18s
|+++ | 6 % ~17s
|++++ | 7 % ~17s
|++++ | 8 % ~17s
|+++++ | 9 % ~17s
|+++++ | 10% ~16s
|++++++ | 11% ~16s
|+++++++ | 12% ~16s
|+++++++ | 13% ~16s
|++++++++ | 14% ~16s
|++++++++ | 16% ~15s
|+++++++++ | 17% ~15s
|+++++++++ | 18% ~15s
|++++++++++ | 19% ~15s
|++++++++++ | 20% ~15s
|+++++++++++ | 21% ~15s
|++++++++++++ | 22% ~14s
|++++++++++++ | 23% ~14s
|+++++++++++++ | 24% ~14s
|+++++++++++++ | 26% ~14s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~13s
|+++++++++++++++ | 29% ~13s
|+++++++++++++++ | 30% ~13s
|++++++++++++++++ | 31% ~13s
|+++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|++++++++++++++++++ | 34% ~12s
|++++++++++++++++++ | 36% ~12s
|+++++++++++++++++++ | 37% ~12s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~11s
|++++++++++++++++++++++ | 42% ~11s
|++++++++++++++++++++++ | 43% ~10s
|+++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~10s
|++++++++++++++++++++++++ | 48% ~10s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~09s
|+++++++++++++++++++++++++++ | 52% ~09s
|+++++++++++++++++++++++++++ | 53% ~09s
|++++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~08s
|+++++++++++++++++++++++++++++ | 58% ~08s
|++++++++++++++++++++++++++++++ | 59% ~08s
|++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~07s
|+++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|+++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s
Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~21s
|+ | 2 % ~21s
|++ | 3 % ~21s
|++ | 4 % ~21s
|+++ | 5 % ~20s
|+++ | 6 % ~20s
|++++ | 7 % ~20s
|++++ | 8 % ~20s
|+++++ | 9 % ~20s
|+++++ | 10% ~20s
|++++++ | 11% ~19s
|++++++ | 12% ~19s
|+++++++ | 13% ~19s
|+++++++ | 14% ~19s
|++++++++ | 15% ~19s
|++++++++ | 16% ~18s
|+++++++++ | 17% ~18s
|+++++++++ | 18% ~18s
|++++++++++ | 19% ~18s
|++++++++++ | 20% ~17s
|+++++++++++ | 21% ~17s
|+++++++++++ | 22% ~17s
|++++++++++++ | 23% ~17s
|++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~16s
|+++++++++++++ | 26% ~16s
|++++++++++++++ | 27% ~16s
|++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~15s
|+++++++++++++++ | 30% ~15s
|++++++++++++++++ | 31% ~15s
|++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~15s
|+++++++++++++++++ | 34% ~14s
|++++++++++++++++++ | 35% ~15s
|++++++++++++++++++ | 36% ~15s
|+++++++++++++++++++ | 37% ~14s
|+++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~14s
|++++++++++++++++++++ | 40% ~14s
|+++++++++++++++++++++ | 41% ~13s
|+++++++++++++++++++++ | 42% ~13s
|++++++++++++++++++++++ | 43% ~13s
|++++++++++++++++++++++ | 44% ~13s
|+++++++++++++++++++++++ | 45% ~12s
|+++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~12s
|++++++++++++++++++++++++ | 48% ~12s
|+++++++++++++++++++++++++ | 49% ~11s
|+++++++++++++++++++++++++ | 50% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~10s
|+++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|+++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|+++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=22s
DimPlot(dabtram_both_times)
DimPlot(dabtram_both_times, group.by = 'OG_condition')
FeaturePlot(dabtram_both_times, features = c('NGFR', 'EGFR', 'nFeature_RNA'))
#Plot scores as heatmap
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died.pdf')
ggarrange(p1,p2, nrow = 2)
dev.off()
null device
1